120 research outputs found
Enhancements of the Kronos Simulation Package and Database for Geometric Design Planning, Operations and Traffic Management in Freeway Networks/Corridors (Phase III)
No full textThis report summarizes the final results of the research effort to develop a freeway traffic simulator with the capability to evaluate freeway operational strategies, such as traffic-responsive ramp metering and high-occupancy vehicles (HOV) lanes. Researchers first developed an efficient software data structure by adopting a dynamic memory allocation scheme to use the available memory as efficiently as possible. That work also included modifying the existing macroscopic, segment-based modeling structure and developing new types of pipeline segments to facilitate detection modeling and further model enhancements. Based on the new segment-based modeling structure, researchers developed a new simulation module to handle HOV lane traffic flows and extended the simulation procedure for an exclusive HOV lane to handle a network of freeways. Further, the simulation model also incorporates a new module to emulate the traffic-responsive ramp metering algorithm implemented by the Traffic Management Center since the 1980s. The new software structure developed in this research allows the future addition of new metering algorithms without major difficulties. To facilitate the data input process for the expanded simulation features, a new Windows-based user interface was developed using the Delphi software development tool kit. With the new user interface, most of the data input process can be done without exiting the main menu screen.Minnesota Department of TransportationKwon, Eil; Kota, Ramesh; Coyle, Michael; Michalopoulos, Panos; Song, Sejun. (1997). Enhancements of the Kronos Simulation Package and Database for Geometric Design Planning, Operations and Traffic Management in Freeway Networks/Corridors (Phase III). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/152947
Obstacle adaptive approaches for distributed task assignment in autonomous mobile robots
No full textThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer ScienceIt has many challenges to assign a group of mobile robots to individual targets according to the specific constraints. In addition to the group behavior constraints (one-toone or one-to-many) of the task assignment, some of the performance constraints include (1) proximity from robot to target (2) suitability of robot in performing a task and (3) quality of connectivity among the robots. Due to the computational complexities and the nature of the dynamical systems, the task assignment approaches have been developed as distributed and dynamical systems approaches with various simplified assumptions. In this thesis, first, I investigate one of the most recently proposed distributed task assignment approaches (Peter Molnar's approach) that combines target assignment and motion planning in order to minimize the robots travelling distance and overhead cost for its one-to-one target assignment. Second, I find that the approach does not provide an efficient path finding algorithm in the environment with obstacles. It simply uses proximity sensors to direct the robots away from obstacles. Based upon the observation, third, I propose efficient task assignment approaches to minimize the robot's travel distance and overhead cost in an environment with one immobile obstacle of any shape and size. In detail, the thesis addresses 1) obstacle modeling and simplification: Initially the vertices of an obstacle were provided to the robots. The robots reconstruct the obstacle to a rectangle shape that encircles the original obstacle, 2) obstacle decomposition for an adjustment: The reconstructed obstacle is further decomposed in case if there was a robot or target object present within the reconstructed area, and 3) optimal target path calculation: Two approaches are designed for calculating distance from robot to target by taking the reconstructed obstacle into account. Approach 1 calculates the shortest distance from robot to target along the perimeter of the obstacle. Approach 2 further optimizes the path by connecting the robot and target to the shortest distance vertices of the obstacle. The computational overhead and task assignment efficiency of the proposed approaches are compared via MATLAB simulations
Fault recovery port-based fast spanning tree algorithm (FRP-FAST) for the fault-tolerant Ethernet on the arbitrary switched network topology
No full textImproving Network Health Monitoring Accuracy Based on Data Fusion for Software Defined Networking
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AlertBLE: Alert Workzone Hazards using Hybrid Filtering and Machine-Learning-Enabled BLE
No full textCollision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard zones from 5 m (static) to 8.19 m (at 10 km/h), ensuring adequate safety margins across operational speeds. AlertBLE employs K-means clustering to identify line-of-sight (LOS)/NLOS propagation environments, integrating this context as features for supervised learning. Among four evaluated classifiers [K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)], KNN demonstrates optimal performance with an efficiency score of 12.8, balancing 82.74% recall with minimal computational requirements (156-KB memory and 1.01-ms inference). Field evaluation using 44774 samples across diverse outdoor conditions demonstrates 88.63% overall detection accuracy with 63-ms system latency - well below the 250-ms safety threshold. The multilayered error mitigation framework, incorporating temporal smoothing, confidence thresholding, and state machine logic, achieves a 75.6% reduction in combined false positives (FPs) and false negatives (FNs). Despite 170.8% average RSSI degradation under severe NLOS conditions, AlertBLE maintains 82% detection accuracy within the critical 5-m zone. The article also presents a comprehensive security framework addressing BLE vulnerabilities, providing a roadmap for production deployment enhancements
A 25.78125Gbps Bi-directional Transceiver with Framed-Pulsewidth Modulation (FPWM) for Extended Reach Optical Links in 28nm CMOS
No full textWe report 25.78125Gb/s time-based modulation scheme referred to as FPWM that extends the reach of C-band optical links by more than 20km as compared to NRZ. The FPWM is insensitive to component nonlinearity since only two signal levels are used unlike PAM4. Measurements show that FPWM achieves 6dB SNR gain over PAM4 in back-to-back and 8dB gain over NRZ at 15km
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