Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Terahertz data processing for standoff detection of improvised explosive devices

Improvised Explosive Devices (IEDs) are homemade, non-conventional explosive devices, which are used to destruct and incapacitate individuals and property. IEDs are becoming a popular weapon of attack among terrorists and insurgents due to their easy of making and capability to cause major damage. Hence, is has become necessary to develop efficient systems for detecting and disarming these devices. The Terahertz technology which uses electromagnetic radiations between 0.3 THz to 10 THz for imaging is one of the most recently developed detection techniques and is ideally suitable for detection of IEDs and similar devices. Although a lot of work has been done for developing a standoff detection system for detecting IEDs using Terahertz imaging, it is still needed to develop advanced techniques for processing of the THz data. In this thesis, efficient signal processing techniques are developed for standoff, real time and wide area detection of IEDs. The signal processing algorithm is a two stage algorithm where the first stage is a preprocessing stage. In this stage, THz data from a large field is given to the correlation filters which detect hotspots in the field where an IED could be present. This stage avoids the computational burden of processing data from the entire field in the second stage. In the second stage, THz data from the hotspots of stage one are unmixed to find the individual explosive materials in each data point/pixel. The unmixing is done using a variant of the Independent Component Analysis algorithm which separates only the required component. Once the components are separated, they are analyzed to see if any of them matches an explosive. Thus, the presence of an IED or explosive can be accurately determined within the field --Abstract, page iii

Research on performance enhancement for electromagnetic analysis and power analysis in cryptographic LSI

制度:新 ; 報告番号:甲3785号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6161Waseda Universit

Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets