20426558
The ANSER Project
6014517
The Real-Time Development and Deployment of a Cooperative Multi-UAV System
This paper presents a systems view of the Autonomous Nav- igation and Sensing Experimental Research (ANSER) project. ANSER is one of the most advanced UAV projects of its kind, which is aimed at demonstrating Decentralised Data Fusion (DDF) and Simultaneous Localisation and Map (SLAM) building on multiple cooperative UAVs. The demonstration of DDF and SLAM require both navigation and terrain sensors to be carried onboard by the UAVs. These include an INS/GPS navigation system, millimeter wave (MMW) radar, and both a single vision node and a vision node augmented with a laser system. This paper presents the implementation of the algorithms, sensors and platforms, along with their interaction to address this demonstration. Furthermore, the paper will present the high fidelity real time simulator which is implemented to thoroughly test all algorithms and sensors before actual implementation in the environment.
36323831
Airborne simultaneous localisation and map building
This paper presents results of the application of simultaneous localisation and map building (SLAM) for an uninhabited aerial vehicle (UAV). Single vision camera and inertial measurement unit (IMU) are installed in a UAV platform. The data taken from a flight test is used to run the SLAM algorithm. Results show that both the map and the vehicle uncertainty are corrected even though the model of the system and observation are highly non-linear. The results, however, also indicate that further work of observability and the relationship between vehicle model drift and the number and the location of landmarks need to be further analysed given the highly dynamic nature of the system.
5884286
Real-time implementation of airborne inertial-SLAM
This paper addresses some challenges to the real-time implementation of Simultaneous Localisation and Mapping (SLAM) on a UAV platform. When compared to the implementation of SLAM in 2D environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. An implementation of airborne SLAM is formulated to relieve this computational complexity in both direct and indirect ways. Our implementation is based on an Extended Kalman Filter (EKF), which fuses data from an Inertial Measurement Unit (IMU) with data from a passive vision system. Real-time results from flight trials are provided.
7701199
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optimization should rely on the quadratic program (QP) or general non-linear program which is known to be computational intensive. In this paper, we propose a gradient descent algorithm for LASSO. Even though the final result is slightly less accurate, the proposed algorithm is computationally simpler than QP or non-linear program, and so can be applied to large size problems. We provide the convergence rate of the algorithm, and illustrate it with simulated models as well as real data sets.
16656258
Robust Anisotropic Disparity Estimation with Perceptual Maximum Variation Modeling
We present a robust anisotropic dense disparity estimation algorithm which employs perceptual maximum variation modeling. Edge-preserving dense disparity vectors are estimated using a coarse-to-fine diffusive method on iteratively filtered images, i.e. the scale-space. While an energy-minimization framework adjusts local disparity, the edges are efficiently preserved by anisotropic disparity-field diffusion. However, the localization at weak image edges which have small brightness variations is fundamentally difficult. In this paper, perceptual maximum variation modeling prevents the delocalization flow over edges, e.g. over-diffusion and back-diffusion, computed by evaluating small variations. We perform disparity-field diffusion on a perceptually optimized color space, which combines the small differences in both brightness and chromaticity. Additionally a consistency constraint is employed in the modeling to avoid the influence of global color distributions and to enhance important edges as the human vision system does. The experimental results show the excellent localization performance preserving the disparity discontinuity of each object.
3831285
Data Integration Hub for a Hybrid Paper Search
In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net.
68452
Danger Theory: The Link between AIS and IDS?
We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new ‘Danger Theory’ (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.
1710897
A low latency router supporting adaptivity for on-chip interconnects
The increased deployment of system-on-chip designs has drawn attention to the limitations of on-chip interconnects. As a potential solution to these limitations, networks-on-chip (NoC) have been proposed. The NoC routing algorithm significantly influences the performance and energy consumption of the chip. We propose a router architecture which utilizes adaptive routing while maintaining low latency. The two-stage pipelined architecture uses look ahead routing, speculative allocation, and optimal output path selection concurrently. The routing algorithm benefits from congestion-aware flow control, making better routing decisions. We simulate and evaluate the proposed architecture in terms of network latency and energy consumption. Our results indicate that the architecture is effective in balancing the performance and energy of NoC designs.