Decision and feature fusion over the fractal inference network using camera and range sensors

Erkmen, İsmet
Erkmen, Aydan Müşerref
Ucar, E
The objective of the ongoing work is to fuse information from uncertain environmental data taken by cameras, short range sensors including infrared and ultrasound sensors for strategic target recognition and task specific action in Mobile Robot applications. Our present goal in this paper is to demonstrate target recognition for service robot in a simple office environment. It is proposed to fuse all sensory signals obtained from multiple sensors over a fully layer-connected sensor network system that provides an equal opportunity competitive environment for sensory data where those bearing less uncertainty, less complexity and less inconsistencies with the overall goal survive, while others fade out. In our work, this task is achieved as a decision fusion using the Fractal Inference Network (FIN), where information patterns or units- modelled as textured belief functions bearing a fractal dimension due to uncertainty - propagate while being processed at the nodes of the network. Each local process of a node generates a multiresolutional feature fusion. In this model, the environment is observed by multisensors of different type, different resolution and different spatial location without a prescheduled sensing scenario in data gathering. Node activation and flow control of information over the FIN is performed by a neuro-controller, a concept that has been developed recently as an improvement over the classical Fractal Inference Network. In this paper, the mathematical closed form representation for decision fusion over the FIN is developed in a way suitable for analysis and is applied to a NOMAD mobile robot servicing an office environment.


Decision making in tracking applications by using dempster-shafer theory /
Turhan, Hasan İhsan; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2014)
The aim of this thesis is to study attribute data fusion and decision making for targets tracked by a sensor network consisting of several radars. As an application deciding both target class and identity are studied. Since only partial information is available, Dempster-Shafer theory is used for this application to assign and combine probability masses. In this study, we focus on the problems of basic probability assignment and decision/data fusion. Classification of air vehicles according to their type is...
Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data
Kucukkececi, Cihan; Yazıcı, Adnan (Institute of Electrical and Electronics Engineers (IEEE), 2019-01-01)
Wireless Multimedia Sensor Networks (WMSN), for object tracking, have been used as an emerging technology in different application areas, such as health care, surveillance, and traffic control. In surveillance applications, sensor nodes produce data almost in real-time while tracking the objects in a critical area or monitoring border activities. The generated data is generally treated as big data and stored in NoSQL databases. In this paper, we present a new object tracking approach for surveillance applic...
Identification and localization on a wireless magnetic sensor network
Baghaee, Sajjad; Uysal Bıyıkoğlu, Elif; Gürbüz, Sevgi Zübeyde; Department of Electrical and Electronics Engineering (2012)
This study focused on using magnetic sensors for localization and identification of targets with a wireless sensor network (WSN). A wireless sensor network with MICAz motes was set up utilizing a centralized tree-based system. The MTS310, which is equipped with a 2-axis magnetic sensor was used as the sensor board on MICAz motes. The use of magnetic sensors in wireless sensor networks is a topic that has gained limited attention in comparison to that of other sensors. Research has generally focused on the d...
Efficiency-aware and energy-aware data collection via a UAV with limited-capacity battery in robotic wireless sensor networks
Gül, Ömer Melih; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2020-12-17)
This thesis investigates efficiency-aware and energy-aware data-collection problems by an unmanned aerial vehicle (UAV) with limited-capacity battery, in a clustered robot network. In each cluster, a cluster head (CH) robot allocates tasks to remaining robots and collects data from them. Firstly, we consider this problem by focusing on minimizing energy consumption of UAV coupled to minimum cost data collection from CH robots by visiting optimal portion of CH robots. UAV decides the CH robots to visit by co...
AKSOY, Yagiz; Alatan, Abdullah Aydın (2014-10-30)
Most of the mobile applications require efficient and precise computation of the device pose, and almost every mobile device has inertial sensors already equipped together with a camera. This fact makes sensor fusion quite attractive for increasing efficiency during pose tracking. However, the state-of-the-art fusion algorithms have a major shortcoming: lack of well-defined uncertainty introduced to the system during the prediction stage of the fusion filters. Such a drawback results in determining covarian...
Citation Formats
İ. Erkmen, A. M. Erkmen, and E. Ucar, “Decision and feature fusion over the fractal inference network using camera and range sensors,” 1998, vol. 3523, Accessed: 00, 2020. [Online]. Available: