3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.

A Nationwide Evaluation of the State of Practice of Performance Measurements for Intelligent Transportation Systems

State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.

Intelligent fitting global real-time task scheduling strategy for high-performance multi-core systems

With the development of high-performance computing,it is possible to solve large-scale computing problems.However,the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation.Improving the performance of a single core makes it challenging to maintain Moore's law,and multi-core processors emerge.A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor.In high-performance computing,the granularity of computing tasks leads to the complexity of scheduling strategies.Satisfying high system performance,load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field,especially in specific multi-core hardware architecture.In this study,global real-time task scheduling is implemented in a high-performance multi-core system.The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy.In the cluster scheduling policy,tasks are allowed to preempt the core with low priority,and the priority of tasks that access memory is dynamically improved,higher than that of all the tasks without memory access.An intelligent fitting method is also proposed.When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold,the priority of the task is promoted to the highest priority,pre-empting the core without the access memory task.The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

Abusive adversarial agents and attack strategies in cyber-physical systems

The exponential increase in IoT device usage has spawned numerous cyberspace innovations.IoT devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for cyberattacks.Most adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost,whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or reward.In this paper,a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control mechanism.It is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the actuator.Experiments on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology,demonstrating three attack strategies.The impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing cyber-vandalism.Finally,the research concludes by highlighting the limitations of the proposed work,followed by recommendations for the future.

Intelligent 3-Way Priority-Driven Traffic Light Control System for Emergency Vehicles

The problem of traffic congestion is a significant phenomenon that has had a substantial impact on the transportation system within the country. This phenomenon has given rise to numerous intricacies, particularly in instances where emergency situations occur at traffic light intersections that are consistently congested with a high volume of vehicles. This implementation of a traffic light controller system is designed with the intention of addressing this problem. The purpose of the system was to facilitate the operation of a 3-way traffic control light and provide priority to emergency vehicles using a Radio Frequency Identification (RFID) sensor and Reduced Instruction Set Computing (RISC) Architecture Based Microcontroller. This research work involved designing a system to mitigate the occurrence of accidents commonly observed at traffic light intersections, where vehicles often need to maneuver in order to make way for emergency vehicles following a designated route. The research effectively achieved the analysis, simulation and implementation of wireless communication devices for traffic light control. The implemented prototype utilizes RFID transmission, operates in conjunction with the sequential mode of traffic lights to alter the traffic light sequence accordingly and reverts the traffic lights back to their normal sequence after the emergency vehicle has passed the traffic lights.

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System(ITS)is essential for effective identification of vulnerable units in the transport network and its stable operation.Also,it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things(IoT).Previous research on vulnerability has no congestion effect on the peak time of urban road network.The cascading failure of links or nodes is presented by IoT monitoring system,which can collect data from a wireless sensor network in the transport environment.The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure(V2I)channels to simulate key segments and their failure probability.Finally,the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors.The two indices are standardized by calculating the relative change rate,and the comprehensive index of the consequence after road network unit is in a failure state.Therefore,by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state,the comprehensive vulnerability of road network can be evaluated by a risk calculation formula.In short,the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.

Integrative Stability Analysis for A Class of Intelligent Control Systems

In this paper the integrative stability is studied for a class of intelligent control systems which are described by an octette structural model. Based on the definitions Of state reachability and stabilizability of intelligent control systems the analysis method and criterion of integrative stability are given.

Qinghua Wentong Intelligent Office Systems

Along with the rapid progress of computer technology, it has become a natural trend that computers are used to handle office routine work to realize office automation. Offered by the Beijing Qinghua Wentong Information Technology Company, the Qinghua Wentong intelligent office system is a newly developed and integrated

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society

Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence(AI).However,the current networked unmanned systems are primarily designed for and applicable to a narrow range of domain-specific missions,and do not have sufficient human-level intel-ligence and human needs fulfillment for the challenging missions in our lives.We propose in this paper a vision of human-centric networked unmanned systems:Unmanned Intelligent Cluster(UnIC).Within this vision,distributed unmanned systems and humans are connected via knowledge sharing and social awareness to achieve collaborative cognition.This paper details UnIC’s concept,sources of intelligence,and layered architecture,and reviews enabling technologies for achieving this vision.In addition to the technological aspects,the social acceptance issues are highlighted.

Research on Generalized Computing Systems

This paper presents a kind of artificial intelligent system-generalized computing system (GCS for short), and introduces its mathematical description, implement problem and learning problem.

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s life.VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world.VANET is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in location.So,effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET.At the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a traveler.With this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for VANET.The presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network.TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization.TLBO is applied to avoid congestion on roadways.Besides,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network.The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

I-Quiz:An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Optimal control and energy storage for DC electric train systems using evolutionary algorithms

Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy.Environmental concerns demand reduction in energy use and peak power demand of railway systems.Furthermore,high transmission losses in DC railway systems make local storage of energy an increasingly attractive option.An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size,charge/discharge power limits,timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption.Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30%depending on the train driving style,and reduced power peaks.