Dynamic Real-time Environments Research Articles

Selfredepth

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems; however, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest highlighting a need for methods that can effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting of full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach’s real-time performance on real-world datasets shows that it outperforms state-of-the-art methods in denoising and restoration performance at over 30 fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.

Patient-centered perspectives on perioperative care

IntroductionThe collection and evaluation of patient-reported outcomes is essential to the development of patient and family centered care. Current patient surveying techniques are limited by delayed response times and restriction to specific health systems. The use of random-domain intercept technology (RDIT), by Real-Time Interactive World-Wide Intelligence (RIWI, Toronto, ON, Canada) mitigates current barriers by creating a dynamic real-time feedback environment and providing a mass sampling technique.MethodsRDIT was employed to survey a wide sample of respondents across the United States (US). Respondents who self-identified as having had a surgical procedure or cared for someone having a surgical procedure were included in the analysis.Results1,004 participants completed the survey and answered questions regarding demographics, perioperative details, sentiments on postoperative recovery, postoperative clinical endpoints, sentiments on healthcare professionals, and opinions on future surgical care.DiscussionThe results of this cross-sectional study identified areas with potential for improvement in the patient perioperative experience that could improve the patient experience. This novel use of RDIT provided a valuable tool for real-time feedback and mass sampling allowing the creation of a dynamic healthcare environment that fosters timely and targeted improvements to patient experiences and outcomes.

Fast move: A prioritized vehicle rerouting strategy in smart city

Efficient and dynamic traffic management is an integral part of any smart city. Finding the shortest route considering congestion in real-time and managing traffic signals determine the efficiency of overall traffic flow. Route planning within a dynamic traffic environment remains an open challenge due to some essential unaddressed issues. Specially, considering priority vehicles to provide better traffic flow. For example, the routes of the vehicles need to be derived in such a way that higher priority vehicles like ambulances, police cars, etc. are provided efficient routes and green signals as per their given priority. To establish the solution we need to consider two factors. Vehicle rerouting to obtain shorter path and traffic signal optimization for lesser waiting time by a vehicle on a traffic signal. Existing works don't consider both factors in their work and tried to solve the issue reactively; upon request for clearance by a vehicle on a particular traffic signal. The proposed work seems to be a proactive approach that considers both factors. In this work, a dynamic real-time traffic environment with prioritized vehicles is modeled as a bi-level optimization problem. The solution ensures that the routes are offered to the vehicles in a way such that the unit travel time of a vehicle with higher priority must be less compared to vehicles with lesser priority. In addition, the traffic signal settings are optimized in a coordinated way to achieve the above-mentioned goal. Rigorous experimentation is done for validation of the proposed solution. Several traffic situations consisting of inherent dynamism are simulated through SUMO and the experimental findings substantiate our claims in terms of various traffic metrics.

Two-layer coordinated reinforcement learning for traffic signal control in traffic network

Intersection traffic signal control considering vehicle emissions has become an important topic, however, the decision complexity of traffic signal control increases dramatically in a dynamic traffic environment with multi-intersections. It is a severe challenge to coordinate traffic signals at multi-intersections based on Internet of Things information to improve the traffic condition of the road network. This paper proposes a two-layer coordination algorithm based on multi-agent reinforcement learning—Multi-agent Coordinated Policy Optimization (MACoPO), for solving traffic signal control at multi-intersections. MACoPO consists of local cooperation, which adjusts the weights of individual rewards and neighborhood agents' rewards by using local cooperation factors (LCF), and global coordination, which updates the LCF to maximize global rewards. The state and reward functions are designed in terms of the current state of the signal, waiting queue length, vehicle density and emission concentration in the lane, vehicle delay, and vehicle emissions, thus making full use of the intersection state information. The proposed method is extensively assessed through simulation experiments using artificial and real road networks and the numerical results confirm its effectiveness in complex and dynamic real-time traffic environments with multi-intersections.

Optimising local map updates in a dynamic environment using an object detection method

Keeping maps up to date in a time-varying environment is a big challenge in the development of autonomous mobile robots. In this paper, the authors propose a new solution to update the local map of the robot in a dynamic environment in real time. This proposed method works based on a combination of lidar scan data and object recognition and positioning algorithms to allow the robot to update the map continuously. Laser scan data for the same part of the environment at different times are compared to perform change detection. When there are enough changes in a location, the map will be edited to remove old features or add new features. To optimise updates for moving objects, authors use object recognition and positioning to decide which objects should be added to the map. The results are displayed in a simulated environment, showing the ability to maintain an up-to-date map despite long-term environmental changes.

A Resilient Prosumer Model for Microgrid Communities with High PV Penetration

Worldwide energy shortages and the green energy revolution have triggered an increase in the penetration of standalone microgrids. However, they have limited generation capacity and are wasteful when excess generated energy is curtailed. This presents an opportunity for the coordinated operation of multiple prosumer microgrids that absorb this waste to enhance their resiliency and reliability. This paper proposes a reliable prosumer model with an inbuilt energy management system (EMS) simulator that considers the mentioned deficiencies for constructing resilient interconnected prosumer microgrids. The EMS simulator operates in a real-time dynamic environment to coordinate the prosumer components and performs flexible switching for (1) prioritizing critical load by shedding the non-critical load and (2) meeting load demand locally or from other interconnected prosumers. The EMS simulator maintains energy balance by setting limits for the battery energy storage system (BESS) to preserve energy during low generation and performs real-time monitoring. The novelty of this model lies in its simplicity and flexibility, which allows interconnected prosumer microgrids to operate in cooperation without the need for communication. The proposed model is evaluated in a post-disaster off-grid scenario using a single-phase average value model that considers reasonable computation time. The simulation results of the proposed system indicate the preservation of stored energy while maintaining critical resources beyond three days of poor weather conditions.

A Plasma-Switch Impedance Tuner With Microsecond Reconfiguration

With many frequency bands previously allocated solely for radar now being reallocated for sharing, radar transmitters must be frequency-agile, able to quickly change operating frequency while maintaining detection range. This article details the design of a 2–4-GHz reconfigurable impedance tuner using 35-W plasma switches. The tuner consists of switched shunt capacitor and inductor branches loading a series microstrip line. For many RF matching applications, a reconfigurable impedance tuner must have a broad tuning bandwidth, real-time switching speeds, high power handling, low loss, and broad Smith chart coverage. The implementation of the tuner using semiconductor plasma switches illuminated by laser diodes is described. The tuner is demonstrated in measurements using a custom search algorithm, adjusting and optimizing the tuner settings to provide maximum output power in approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300~\mu \text{s}$ </tex-math></inline-formula> upon changes in operating frequency or antenna impedance. This tuner provides superior reconfiguration times over other high-power tuners in the literature. The tuner possesses losses as low as 0.77 dB and at least 30% Smith chart coverage over the entire tunable bandwidth. With a 26.7- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> switching speed, the impedance tuner is also demonstrated to handle 20 W of power at all frequencies and states with over 40 W of power for greater than half of the octave bandwidth and power handling measured as high as 70 W. The combination of fast reconfiguration and high power handling in an impedance tuner can enable cognitive radar systems to adapt to a congested and dynamic environment in real time.

Path Planning for the Dynamic UAV-Aided Wireless Systems Using Monte Carlo Tree Search

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach

ObjectiveThis work aims to establish a framework in measuring the various attentional levels of the human operator in a real-time animated environment through a visual neuro-assisted approach. BackgroundWith the increasing trend of automation and remote operations, understanding human-machine interaction in dynamic environments can greatly aid to improve performance, promote operational efficiency and safety. MethodTwo independent 1-hour experiments were conducted on twenty participants where eye-tracking metrics and neuro activities from electroencephalogram (EEG) were recorded. The experiments required participants to exhibit attentive behaviour in one set and inattentive in the other. Two segments (“increasing flight numbers” and “relatively constant flight numbers”) were also extracted to study the participants’ visual behavioral differences in relation to aircraft numbers. ResultsFor the two experimental studies, those in the attentive behavioral study show incidences of higher fixation count, fixation duration, number of aircraft spotted, and landing fixations whereas those in inattentive behavior study reveal higher zero-fixation frame count. In experiments involving ‘increasing flight numbers’, a higher percentage of aircraft were spotted as compared to those with ‘constant flight numbers’ in both the groups. Three parameters (number of aircraft spotted, and landing fixations and zero-fixation frame count) are newly established. As radar monitoring is a brain engagement activity, positive EEG data were registered in all the participants. A newly Task Engagement Index (TEI) was also formulated to predict different attentional levels. ConclusionResults provide a refined quantifiable tool to differentiate between attentive and inattentive monitoring behavior in a real-time dynamic environment, which can be applied across various sectors. RecommendationWith the quantitative TEI established, this paves the way for future studies into attentional levels by regions, time based, as well as eye signature studies in relation to visual task engagement and management and determining expertise levels to be explored. Factors relating to fatigue could also be investigated using the TEI approach proposed.

Multi-Classes and Motion Properties for Concurrent Visual SLAM in Dynamic Environments

Working in a dynamic environment is a challenging problem for visual simultaneous localization and mapping (visual SLAM). Most of the existing visual SLAM algorithms fail resulting in significant error or losing in tracking when moving objects dominate the scene. We found two reasons cause these issues: (i) Previous approaches use information from all regions in the image; (ii) Existing algorithms use just two groups and block all feature points from moveable objects. In this paper, we propose a novel Multi-classes and motion properties for Concurrent Visual SLAM (MCV-SLAM) algorithm, which defines classes into five categories and concurrently fuses prior knowledge and observation of moving objects with semantic segmentation to ensure visual SLAM works properly for dynamic environments in real time. We also propose an adaptive method to optimize camera pose by using more potential inlier feature points with continuous weights, while eliminating the impact of moving objects. Our experiments are performed on public datasets of both indoor and outdoor scenes with moving objects in dynamic environments. The experimental results demonstrate that our method outperforms previous works with greater robustness and smaller tracking errors, and our MCV-SLAM can deal with the situations (i.e., the dominance of moving objects, lack of matching points), which lead misestimating occurs in existing SLAMs.

Differential Pressure-Based Densitometer in Dynamic Condition

Change in density will cause errors in measurement for indirect-based level sensing approaches, such as capacitive sensors. In addition, there is growing attention in measuring density accurately not only in laboratory conditions but also in a real-time dynamic environment. To address the above-mentioned issues, an independent instrument based on differential pressure sensors is proposed in this article. Synchronous detection is used to detect the desired signal by calculating the coefficients of the digital Fourier transform. The characterization of the sensor is experimentally determined under a steady-state condition, where a linear response is observed. The thermal behavior of the proposed sensor is studied and compensated using the polynomial fitting technique. Evaluating the uncertainty due to reference values, linearity, and repeatability, the obtained results show a combined uncertainty lower than 7.5 (mg/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), mainly limited by observed asymmetric hysteresis at higher temperatures.

Resource Management in Cloud Radio Access Network: Conventional and New Approaches.

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

Research on Test Method of Satellite Navigation Signal Simulator

The method of the current acceleration range navigation satellite signal simulator detection theoretically analyzed, but in the actual test work, how to set the satellite navigation signal simulator motion scenes and satellite receivers, a smaller level of the RF test signal navigation As well as implementing automated testing, there is a lack of practical and feasible testing methods. This paper simulates the RF signals received by receivers in different dynamic environments in real time, builds a dynamic range test staging, forms a specific test plan, and carries out the validity, practicability and effectiveness of the test verification test method. This test method is simple and reliable. The dynamic range of the satellite navigation signal simulator in the simulation environment can be accurately detected by this method.

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated.

A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment

Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

A Survey of Learning Classifier Systems in Games [Review Article

Games are becoming increasingly indispensable, not only for fun but also to support tasks that are more serious, such as education, strategic planning, and understanding of complex phenomena. Computational intelligence-based methods are contributing significantly to this development. Learning Classifier Systems (LCS) is a pioneering computational intelligence approach that combines machine learning methods with evolutionary computation, to learn problem solutions in the form of interpretable rules. These systems offer several advantages for game applications, including a powerful and flexible agent architecture built on a knowledge-based symbolic modeling engine; modeling flexibility that allows integrating domain knowledge and different machine learning mechanisms under a single computational framework; an ability to adapt to diverse game requirements; and an ability to learn and generate creative agent behaviors in real-time dynamic environments. We present a comprehensive and dedicated survey of LCS in computer games. The survey highlights the versatility and advantages of these systems by reviewing their application in a variety of games. The survey is organized according to a general game classification and provides an opportunity to bring this important research direction into the public eye. We discuss the strengths and weaknesses of the existing approaches and provide insights into important future research directions.

Bio-inspired heterogeneous architecture for real-time pedestrian detection applications

Along with the development of powerful processing platforms, heterogeneous architectures are nowadays permitting new design space explorations. In this paper, we propose a novel heterogeneous architecture for reliable pedestrian detection applications. It deploys an efficient Histogram of Oriented Gradient pipeline tightly coupled with a neuro-inspired spatio-temporal filter. By relying on hardware–software co-design principles, our architecture is capable of processing video sequences from real-word dynamic environments in real time. The paper presents the implemented algorithm and details the proposed architecture for executing it, exposing in particular the partitioning decisions made to meet the required performance. A prototype implementation is described and the results obtained are discussed with respect to other state-of-the-art solutions.

Predicting real-time adaptive performance in a dynamic decision-making context

AbstractIndividuals in organizations must frequently enact a series of ongoing decisions in real-time dynamic contexts. Despite the increasing need for individuals to manage dynamic decision-making demands, we still understand little about individual differences impacting performance in these environments. This paper proposes a new construct applicable to adaptation in such real-time dynamic environments. Cognitive agility is a formative construct measuring the individual capacity to exhibit cognitive flexibility, cognitive openness and focused attention. This study predicts that cognitive agility will impact adaptive performance in a real-time dynamic decision-making microworld computer game called the Networked Fire Chief; a simulation developed to study and train Australian fire fighters. Cognitive agility, operationalized through three distinct methods (performance measures, self-reports and external-rater reports), explained unique variance beyond measures of general intelligence on the total score of adaptive performance in the microworld.

A Neurally Controlled Computer Game Avatar With Humanlike Behavior

This paper describes the NeuroBot system, which uses a global workspace architecture, implemented in spiking neurons, to control an avatar within the Unreal Tournament 2004 (UT2004) computer game. This system is designed to display humanlike behavior within UT2004, which provides a good environment for comparing human and embodied AI behavior without the cost and difficulty of full humanoid robots. Using a biologically inspired approach, the architecture is loosely based on theories about the high-level control circuits in the brain, and it is the first neural implementation of a global workspace that has been embodied in a complex dynamic real-time environment. NeuroBot's humanlike behavior was tested by competing in the 2011 BotPrize competition, in which human judges play UT2004 and rate the humanness of other avatars that are controlled by a human or a bot. NeuroBot came a close second, achieving a humanness rating of 36%, while the most human human reached 67%. We also developed a humanness metric that combines a number of statistical measures of an avatar's behavior into a single number. In our experiments with this metric, NeuroBot was rated as 33% human, and the most human human achieved 73%.

Trusted interaction approach for dynamic service selection using multi-criteria decision making technique

Recent developments in information technology have shifted the computing paradigm in a more dynamic direction, and this has raised new challenges. In a dynamic computing environment, (1) the number of transacting entities is not fixed; (2) the relationship between these entities are very dynamic; (3) transacting entities may not necessarily have previous knowledge of each other; (4) the surrounding context may possibly constantly change; and, (5) it is possible that the transaction is conducted in fully a automatic approach. Based on these unique feature in a dynamic environment, we claim that two important challenges need to be resolved: one is transacting entities should be able to establish trusted interactions between each other, and another is transacting entities should be able to select the most suitable transacting entities by pre-programmed business rules and current context. Based on our previous research work on MobiPass, this paper proposes a technique which successfully solves the above two important research issues by using Multi Criteria Decision Making (MCDM) on top of the MobiPass framework, to help transacting entities select the most suitable transacting partners under a trusted interaction in dynamic environments in real time.