<p>In the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved system performance over unmanaged sensing approaches through the intelligent control of the available sensors. In particular, information-theoretic sensor management approaches have shown promise for providing robust and effective sensor manager performance.</p><p>This work develops information-theoretic sensor managers for a general static target detection problem. Two types of sensor managers are developed. The first considers a set of discrete objects, such as anomalies identified by an anomaly detector or grid cells in a gridded region of interest. The second considers a continuous spatial region in which targets may be located at any point in continuous space. In both types of sensor managers, the sensor manager uses a Bayesian, probabilistic framework to model the environment and tasks the sensor suite to make new observations that maximize the expected information gain for the system. The sensor managers are compared to unmanaged sensing approaches using simulated data and using real data from landmine detection and unexploded ordnance (UXO) discrimination applications, and it is demonstrated that the sensor managers consistently outperform the unmanaged approaches, enabling targets to be detected more quickly using the sensor managers. The performance improvement represented by the rapid detection of targets is of crucial importance in many static target detection applications, resulting in higher rates of advance and reduced costs and resource consumption in both military and civilian applications.</p> / Dissertation

The problem of subsurface imaging with Ground Penetrating Radar (GPR) is a challenging one. Due to the low-pass nature of soil sensors must utilise wave-lengths that are of the same order of magnitude as the object being imaged. This makes imaging difficult as straight ray approximations commonly used in higher frequency applications cannot be used. The problem becomes even more challenging when the target is shallowly buried as in this case the ground surface reflection and the near-field parameters of the radar need to be considered. This thesis has investigated the problem of imaging shallowly buried targets with GPR. Two distinct problems exist in this field radar design and the design of inverse scattering techniques. This thesis focuses on the design of inverse scattering techniques capable of taking the electric field measurements from the receiver and providing accurate images of the scatterer in real time. The thesis commences with a brief introduction to GPR theory. It then provides an extensive review of linear inverse scattering techniques applied to raw GPR data. As a result of this review the thesis draws the conclusion that, due to its strong foundations in Maxwell's equations, diffraction tomography is the most appropriate approach for imaging shallowly buried targets with GPR. A three-dimensional diffraction tomographic technique is then developed. This algorithm forms the primary contribution of the thesis. The novel diffraction tomography technique improves on its predecessors by catering for shallowly buried targets, significant antenna heights and evanescent waves. This is also the first diffraction tomography technique to be derived for a range of antenna structures. The advantages of the novel technique are demonstrated first mathematically then on synthetic and finally practical data. The algorithm is shown to be of high practical value by producing accurate images of buried targets in real time.

<p>The goal of a machine learning problem is to learn useful patterns from observations so that appropriate inference can be made from new observations as they become available. Based on whether labels are available for training data, a vast majority of the machine learning approaches can be broadly categorized into supervised or unsupervised learning approaches. In the context of supervised learning, when observations are available as labeled feature vectors, the learning process is a well-understood problem. However, for many applications, the standard supervised learning becomes complicated because the labels for observations are unavailable as labeled feature vectors. For example, in a ground penetrating radar (GPR) based landmine detection problem, the alarm locations are only known in 2D coordinates on the earth's surface but unknown for individual target depths. Typically, in order to apply computer vision techniques to the GPR data, it is convenient to represent the GPR data as a 2D image. Since a large portion of the image does not contain useful information pertaining to the target, the image is typically further subdivided into subimages along depth. These subimages at a particular alarm location can be considered as a set of observations, where the label is only available for the entire set but unavailable for individual observations along depth. In the absence of individual observation labels, for the purposes of training standard supervised learning approaches, observations both above and below the target are labeled as targets despite substantial differences in their characteristics. As a result, the label uncertainty with depth would complicate the parameter inference in the standard supervised learning approaches, potentially degrading their performance. In this work, we develop learning algorithms for three such specific scenarios where: (1) labels are only available for sets of independent and identically distributed (i.i.d.) observations, (2) labels are only available for sets of sequential observations, and (3) continuous correlated multiple labels are available for spatio-temporal observations. For each of these scenarios, we propose a modification in a traditional learning approach to improve its predictive accuracy. The first two algorithms are based on a set-based framework called as multiple instance learning (MIL) whereas the third algorithm is based on a structured output-associative regression (SOAR) framework. The MIL approaches are motivated by the landmine detection problem using GPR data, where the training data is typically available as labeled sets of observations or sets of sequences. The SOAR learning approach is instead motivated by the multi-dimensional human emotion label prediction problem using audio-visual data, where the training data is available in the form of multiple continuous correlated labels representing complex human emotions. In both of these applications, the unavailability of the training data as labeled featured vectors motivate developing new learning approaches that are more appropriate to model the data. </p><p>A large majority of the existing MIL approaches require computationally expensive parameter optimization, do not generalize well with time-series data, and are incapable of online learning. To overcome these limitations, for sets of observations, this work develops a nonparametric Bayesian approach to learning in MIL scenarios based on Dirichlet process mixture models. The nonparametric nature of the model and the use of non-informative priors remove the need to perform cross-validation based optimization while variational Bayesian inference allows for rapid parameter learning. The resulting approach is highly generalizable and also capable of online learning. For sets of sequences, this work integrates Hidden Markov models (HMMs) into an MIL framework and develops a new approach called the multiple instance hidden Markov model. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. The resulting approach is highly generalizable and also capable of online learning. Similarly, most of the existing approaches developed for modeling multiple continuous correlated emotion labels do not model the spatio-temporal correlation among the emotion labels. Few approaches that do model the correlation fail to predict the multiple emotion labels simultaneously, resulting in latency during testing, and potentially compromising the effectiveness of implementing the approach in real-time scenario. This work integrates the output-associative relevance vector machine (OARVM) approach with the multivariate relevance vector machine (MVRVM) approach to simultaneously predict multiple emotion labels. The resulting approach performs competitively with the existing approaches while reducing the prediction time during testing, and the sparse Bayesian inference allows for rapid parameter learning. Experimental results on several synthetic datasets, benchmark datasets, GPR-based landmine detection datasets, and human emotion recognition datasets show that our proposed approaches perform comparably or better than the existing approaches.</p> / Dissertation

Explosives have become increasingly common on the battlefield worldwide. Military personnel and civilians often experience blast loading to the lower extremity due to its direct contact with the ground and floor of vehicles. The pressure and axial loading from these incidents often lead to detrimental injuries. These injuries can be due to a number of mechanisms terming them primary, secondary, tertiary, or quaternary blast injuries. Of these injuries, this study will focus on primary and tertiary injuries, specifically bone fractures, compartment syndrome, and soft tissue disruption which often result from blast loading due to these mechanisms. However, the pressure and load levels causing these injuries are unknown. Currently, the methodologies, which study the injury criteria and design of blast mitigating structures, are limited. The main limitations are the lower rates of testing (automobile), specimen limitation (cadavers, surrogates, etc.), cost, and testing repeatability. Consequently, the goal of this dissertation is to develop a realistic computational model which can be used to improve the injury criteria, personal protective equipment (PPE), and vehicular structure in a cost effective and timely manner. Three Aims were thus pursued. For Specific Aim 1, a standing lower extremity was developed, verified, and simulated with several open-air blast loading conditions. Specific Aim 2 focused on validating the lower extremity model using experimental drop tower test results. In the drop tower simulation, the lower extremity model was successfully converted into a seated posture model and setup with similar loading and boundary conditions as the experiment. Specific Aim 3 involved incorporating a boot into the standing lower extremity model and evaluating its ability to mitigate pressure waves. In summary, Specific Aims 1 and 2 developed, verified, and validated a realistic human lower extremity model for the use in blast simulations. Specific Aim 3 further confirmed the models use in developing PPE.

Land mines are a major humanitarian problem with millions of active mines in place around the world. Since these mines can have little metal in them, novel detection techniques are needed. Nuclear Quadrupole Resonance (NQR) is one such technique. Unfortunately, NQR is highly succeptible to radio frequency interference (RFI). A significant contribution of this thesis is the development of a custom, experimental data acquisition system designed and built specifically for capturing RFI at frequencies significant to NQR land mine detection systems. Another major contribution is the development of data analysis techniques for determining the number of reference antennas required to effectively cancel out RFI at frequencies and in environments typical of an NQR land mine detection system.

武裝衝突法已成為各交戰國(方)爭取國際支持之重要手段，軍隊執行作戰任務，必須遵守武裝衝突法及國際法的相關規範，始可確保作戰行動的合法性及正當性。陸戰法規是一系列戰爭法規則和慣例的重要歷史淵源，為武裝衝突法之主體。隨著以規範交戰規則為主的海牙法體系與規範人道保護規則為主的日內瓦法體系逐漸整合，陸戰法規除規範作戰使用武器的規則外，其他有關交戰者、敵對行為及軍事佔領等規則均納入1949年日內瓦公約及1977年的兩項附加議定書。 觀察國際社會對陸戰法規實踐的面向，除可從戰史例證中得到驗證外，尚可從童兵、地雷、武裝衝突遺留爆炸物處理及文化資產保護等議題，得以進一步瞭解陸戰法規實踐的全貌。要禁止利用童兵的行為，除了國家行為者應負履約義務外，對非國家行為者侵犯兒童權利行為的制止是當務之急，國際刑事法院應扮演更積極的角色。2003年波灣戰爭中，《禁雷公約》雖未能阻止伊拉克使用人員殺傷雷，但已顯示該公約對非締約國產生的隱性約束力，禁用人員殺傷雷的規範雖未完全實現，但已為多數國家認同，未來可能成為一項習慣國際法。《戰爭遺留爆炸物議定書》雖可為解決武裝衝突結束後平民所面臨的主要威脅提供一項法律制度，但因條約內容強制性不足，成效尚難顯現。目前對文化資深保護的重點在於免遭武裝衝突毀損為主要議題，因武裝衝突而流落異域之文化資產回復或返還，可能成為未來發展之主要議題。 / The Law of Armed Conflict has become one of the important means to win the international support for all belligerent. The army must comply with the Law of Armed Conflict in combat, in order to ensure the legitimacy of military operation. The law of war on land has its historical origins for regulations and customary of the Law of War, and also has codified as the main body of the Law of Armed Conflict. The Law of The Hague and the Law of Geneva have been gradually integrated. All these regulations of the law of war on land about combatant, hostilities and military occupation were included in the “Geneva Conventions of 1949” and two “Additional Protocols of 1977”, except the regulations of the use of the weapon. Observing the different aspects of the law of war on land in the international community from the cases above, furthermore, the issues of “Child Soldier”, “Landmine”, “Explosive Remnants of Armed Conflict” and “Protection of Cultural Property in the Event of Armed Conflict”, can help to understand the full view of the practice of the law of war on land. For stopping the use of child soldiers, the obligation of convention should be executed by the state actors, the task of top priority should prevent the infringements of the right of child of the non-state actors, and the International Criminal Court should play a more positive role. In the “Gulf War 2003”, the rule, “Convention on Prohibition of Use, Stockpiling, Production and Transfer of Anti-personnel Landmines and on Their Destruction”, although it has not prevent Iraq from the use of anti-personnel landmine, had showed the indistinct effect of the convention to the powers in the conflict that may not be parties to the convention. Although the rule of prohibition of use of anti-personnel mines has not completely realized, it had already been approved by most states, and may become one of the international customary in the future. The “Protocol on Explosive Remnants of War annexed to the “Protocol on Explosive Remnants of War (Protocol V to the 1980 Convention)” provided a legal protection for the civilian after the armed conflict. Its effect was still too difficult to manifest, because the force of provisions of the protocol were insufficient. The protection of cultural property in the event of armed conflict focuses on exempting from the damage of cultural property at present, but the issue like how to recover the cultural properties which were pillaged in armed conflicts, would become the main theme in the foreseeable future.

Humanitarian clearance of minefields is most often carried out by hand, conventionally using a a metal detector and a probe. Detection is a very slow process, as every piece of detected metal must treated as if it were a landmine and carefully probed and excavated, while many of them are not. The process can be safely sped up by use of Ground-Penetrating Radar (GPR) to image the subsurface, to verify metal detection results and safely ignore any objects which could not possibly be a landmine. In this thesis, we explore the possibility of using Full Wave Inversion (FWI) to improve GPR imaging for landmine detection. Posing the imaging task as FWI means solving the large-scale, non-linear and ill-posed optimisation problem of determining the physical parameters of the subsurface (such as electrical permittivity) which would best reproduce the data. This thesis begins by giving an overview of all the mathematical and implementational aspects of FWI, so as to provide an informative text for both mathematicians (perhaps already familiar with other inverse problems) wanting to contribute to the mine detection problem, as well as a wider engineering audience (perhaps already working on GPR or mine detection) interested in the mathematical study of inverse problems and FWI.We present the first numerical 3D FWI results for GPR, and consider only surface measurements from small-scale arrays as these are suitable for our application. The FWI problem requires an accurate forward model to simulate GPR data, for which we use a hybrid finite-element boundary-integral solver utilising first order curl-conforming N\'d\'{e}lec (edge) elements. We present a novel `line search' type algorithm which prioritises inversion of some target parameters in a region of interest (ROI), with the update outside of the area defined implicitly as a function of the target parameters. This is particularly applicable to the mine detection problem, in which we wish to know more about some detected metallic objects, but are not interested in the surrounding medium. We may need to resolve the surrounding area though, in order to account for the target being obscured and multiple scattering in a highly cluttered subsurface. We focus particularly on spatial sensitivity of the inverse problem, using both a singular value decomposition to analyse the Jacobian matrix, as well as an asymptotic expansion involving polarization tensors describing the perturbation of electric field due to small objects. The latter allows us to extend the current theory of sensitivity in for acoustic FWI, based on the Born approximation, to better understand how polarization plays a role in the 3D electromagnetic inverse problem. Based on this asymptotic approximation, we derive a novel approximation to the diagonals of the Hessian matrix which can be used to pre-condition the GPR FWI problem.