En droit international, on a différentes approches du handicap : l’approche médicale qui renvoie à la déficience et à la maladie et l’approche sociale qui renvoie à la personne physique et au citoyen. Tandis que la Convention internationale relative aux droits de la personne handicapée fait de celle-ci un nouveau sujet du droit international, l’OMS, l’UNESCO… quant à elles ont établi des programmes pour encadrer et promouvoir les droits des handicapés. De même, le droit international, en l’occurrence, le droit international des droits l’homme, le droit international humanitaire ont édicté un ensemble des mécanismes, des moyens ou des garanties de protection pour protéger les personnes handicapées. Ainsi, notre première partie a été consacrée à la construction du cadre international de protection du handicap, à savoir l’ensemble des textes, des institutions et des mécanismes de protection universels, régionaux, nationaux ou spécifiques protégeant les personnes handicapées. La deuxième partie analyse l’étendue, la portée ainsi que les différents obstacles de la protection du handicap. Ces obstacles sont d’ordre économique, politique, socioculturel ou juridique. Nous avions terminé notre étude par un aperçu des violations desdits droits, des insuffisances des mécanismes existants et des propositions en vue de l’amélioration de la protection de la personne handicapée. / International protection of disable person is our topic. We have analyzed this topic in two parts.First of all, we have analyzed and presented the international juridical system of protection of the disable persons. In this way, we have study, all the institutions of protection, the laws and, international, regional, national and typical mechanisms of protection provide by human international law and also by humanitarian law. In the second part, we have studied different obstacles with not permit a huge or effective protection of disable and disable person. These obstacles are in different sorts. In this study, we have pick and analyze political, economic, social, cultural, or legal obstacles. In the terms of this study, we give some proposals or solutions in order to ameliorate and improve the protection of these persons.

Approved for public release, distribution is unlimited / Landmines continue to be a threat to both military and civilian communities throughout the world. Current methods of detection, while better than nothing, could certainly be improved. Seismic SONAR is a promising new technology that may help save countless lives. The goal of this thesis was to advance Seismic SONAR development by introducing a mobile source which could be easily used in practical applications. A small tracked vehicle with dual inertial mass shakers mounted on top was used for a source. The source accurately transmitted the shaker signal into the ground, and its mobility made it a practical choice for field operations. It excited Rayleigh waves, as desired, but also generated undesirable P-waves and was not found to be directional. It proved incapable of finding a target. Improvements, such as a deploying an array of mobile sources and a stronger source, should vastly enhance the performance of such tracked vehicles in seismic SONAR mine detection and should be pursued. / Ensign, United States Navy

Instead of state governments, rebel groups have become the most prolific landmine users. However, rebels display significant variation in the way they restrict the use of landmines. While some armed groups seek to limit their effects to government forces, some indiscriminately lay mines irrespective of collateral damage, and others directly target civilians with landmines. Furthermore, some rebels have renounced the use of anti-personnel mines and engage in mine action. In this thesis, I seek to explain this empirical variation. I argue that civilian victimisation caused by landmines creates significant legitimacy costs to rebels, thus increasing incentives to exercise restraint. It follows that the more legitimacy-seeking behaviour rebels display, the more likely they are to exercise restraint on landmine use. I test this hypothesis in a structured focused comparison of three rebel groups from the Philippines. The case studies support the hypothesis, as legitimacy-seeking behaviour had a positive effect on restraint on landmine use in the selected groups. This thesis contributes to the field mainly in two ways. First, I conceptualise and measure restraint on landmine use, which had been neglected in previous studies. Second, I develop a theoretical argument specifically explaining variation in restraint on landmine use.

It is widely recognized that landmines pose a significant threat to the development and recovery of post-conflict societies. What is less well understood is the impact that these weapons have on the everyday lives and wellbeing of affected people and the environments in which they live. This thesis therefore seeks to deepen this understanding by presenting the findings from community-level qualitative research undertaken in Cambodia, one of the most heavily mined countries in the world. I argue that it is essential to consider the effect that landmines have on people, the environments in which they live, and the relationships between people and environment. In order to explore this, I build on the notion of ‘wellbeing ecology’ introduced by White & Jha (2014). Wellbeing ecology is a place-sensitive approach that considers the inter-connected and dynamic social, economic, emotional, physical and spiritual relationships that people have with each other and their environments over time. By their very presence, landmines represent a threat to both social and natural systems. They also reconstitute people’s experience of place. I explore this in particular through the notion of contaminated landscapes, which draws on and takes forward work on therapeutic landscapes in health geography. My data reveals that local people and mine action actors understand the effects of landmines differently. While mine action actors focus predominantly on material impact, local people conceptualise landmine impact in a more holistic way, referring to its social, emotional, spiritual, psychological and physical meanings. Data from the village highlights the importance of place for wellbeing, revealing that living in a contaminated landscape negatively affects people’s quality of life materially, relationally and subjectively. This demonstrates how a wellbeing ecology approach can usefully add to the understanding of the experience of living with landmines and the effect this has on quality of life.

Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

<p>This dissertation addresses the problem of detecting buried explosive threats (i.e., landmines and improvised explosive devices) with ground-penetrating radar (GPR) and hyperspectral imaging (HSI) across widely-varying environmental conditions. Automated detection of buried objects with GPR and HSI is particularly difficult due to the sensitivity of sensor phenomenology to variations in local environmental conditions. Past approahces have attempted to mitigate the effects of ambient factors by designing statistical detection and classification algorithms to be invariant to such conditions. These methods have generally taken the approach of extracting features that exploit the physics of a particular sensor to provide a low-dimensional representation of the raw data for characterizing targets from non-targets. A statistical classification rule is then usually applied to the features. However, it may be difficult for feature extraction techniques to adapt to the highly nonlinear effects of near-surface environmental conditions on sensor phenomenology, as well as to re-train the classifier for use under new conditions. Furthermore, the search for an invariant set of features ignores that possibility that one approach may yield best performance under one set of terrain conditions (e.g., dry), and another might be better for another set of conditions (e.g., wet).</p><p>An alternative approach to improving detection performance is to consider exploiting differences in sensor behavior across environments rather than mitigating them, and treat changes in the background data as a possible source of supplemental information for the task of classifying targets and non-targets. This approach is referred to as context-dependent learning. </p><p>Although past researchers have proposed context-based approaches to detection and decision fusion, the definition of context used in this work differs from those used in the past. In this work, context is motivated by the physical state of the world from which an observation is made, and not from properties of the observation itself. The proposed context-dependent learning technique therefore utilized additional features that characterize soil properties from the sensor background, and a variety of nonparametric models were proposed for clustering these features into individual contexts. The number of contexts was assumed to be unknown a priori, and was learned via Bayesian inference using Dirichlet process priors.</p><p>The learned contextual information was then exploited by an ensemble on classifiers trained for classifying targets in each of the learned contexts. For GPR applications, the classifiers were trained for performing algorithm fusion For HSI applications, the classifiers were trained for performing band selection. The detection performance of all proposed methods were evaluated on data from U.S. government test sites. Performance was compared to several algorithms from the recent literature, several which have been deployed in fielded systems. Experimental results illustrate the potential for context-dependent learning to improve detection performance of GPR and HSI across varying environments.</p> / Dissertation

<p>The ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for buried target detection (BTD). GPR offers excellent detection performance, however, it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR but they do not (yet) achieve comparable detection performance. Another strategy to improve the ROA of the GPR is to combine it with a large standoff sensor within the same BTD system, and to leverage the benefits of the respective modalities. This work investigates both of the aforementioned approaches to improve the ROA of GPR systems using statistical modeling techniques. The first part of the work investigates two large-standoff modalities for BTD systems. New detection algorithms are proposed in both cases with the goal of improving their detection performance so that it is more comparable with the GPR. The second part of the work investigates two methods of combining the GPR with a large standoff modality in order to yield a system with greater ROA, but similar target detection performance. All proposed statistical modeling approaches in this work are tested for efficacy using real field-collected data from BTD systems. The experimental results show that each of the proposed methods contribute towards the goal of improving the ROA of BTD systems.</p> / Dissertation

Ground-Penetrating Radar (GPR) is a popular non-destructive geophysical technique with a wide range of diverse applications. Civil engineering, hydrogeophysics, forensic, glacier geology, human detection and borehole geology are some of the fields in which GPR has been applied with successful or promising results. One of the most mainstream applications of GPR is landmine detection. A lot of methods have been suggested over the years to assist the landmine detection issue. Metal detectors, trained rats or dogs, chemical methods and electrical resistivity tomography are –amongst others– some of the suggested techniques. The non-destructive nature of GPR makes it an attractive choice for a problem such as demining in which contact to the ground is not allowed. The main advantage of GPR is its ability to detect both metallic and non-metallic targets. Furthermore, GPR can provide an insight regarding the nature of the target (e.g. size, burial depth, type). From the above, it is evident that GPR can potentially reduce the false alarms emerging from small metallic objects (e.g. bullets, wires, etc.) usually encountered in battle-fields and industrialised areas. Combining the robustness of the metal detector with the resolution of GPR results in a reliable and efficient detection framework which has been successfully applied in Cambodia and Afghanistan. Despite the promising, and in some cases impressive results, aspects of GPR can be further improved in an effort to optimise GPR’s performance and decrease its limitations. The validation of a GPR system is usually achieved through the so called Receiver Operation Characteristics (ROC) which depicts the probability of detection with respect to the false alarm rate. ROC is a highly nonlinear function which is sensitive to the environment as well as to the antenna unit. Landmines are typically small objects, often less than 10 cm diameter, which are shallow buried, usually in less than 10 cm depth, and sometimes almost exposed. In order for the landmines to be resolved, high frequency antennas are essential. The latter are sensitive to soil’s inhomogeneities, rough surface, water puddles, vegetation and so on. Apart from that, the near field nature of the problem makes the antenna unit part of the medium which contributes to the unwanted clutter. The above, outlines the multi-parametric nature of the problem for which no straightforward approach has yet to be proposed. Numerical modelling is a practical and solid approach to understand the physical behaviour of a system. In the case of GPR for landmine detection, numerical modelling can be a practical tool for designing and optimising antennas in synthetic but nonetheless realistic conditions. Apart from that, evaluation of a processing method only to a specific environment is not a robust approach and does not provide any evidence for its wider inclusivity and limitations. However, evaluation in different conditions can become costly and unpractical. Numerical modelling can tackle this problem by providing data for a wide range of scenarios. An extensive database of simulated responses, apart from being a practical testbed, can be also employed as a training set for machine learning. A multi-variable problem like demining, in order to be addressed using machine learning, requires a large amount of data. These must equally include all possible different scenarios i.e. different landmines, in different media with stochastically varied properties and topography. Additionally, different heights of the antenna and different depths of the landmines must also be examined. Numerical modelling seems to be a practical approach to achieve an equally distributed and coherent dataset like the one briefly described above. Numerical modelling of GPR for landmine detection has been applied in the past using generic antennas in simplified and clinical scenarios. This approach can be used in an educational context just to provide a rough estimation of GPR’s performance. In the present thesis a realistic numerical scheme is suggested in which, simplifications are kept to a minimum. The numerical solver, employed in the suggested numerical scheme, is the Finite- Difference Time-Domain (FDTD) method. Both the dispersive properties and the Absorbing Boundary Condition (ABC) are implemented through novel and accurate techniques. In particular, a novel method which implements an inclusive susceptibility function is suggested and it is shown that surpasses the performance of the previous approaches while retaining their computational efficiency. Furthermore, Perfectly Matched Layer (PML) and more specifically Convolutional Perfectly Matched Layer (CPML) is implemented through a novel time-synchronised scheme which it is proven to be more accurate compared to the traditional CPML with no additional computational requirements. An accurate numerical solver, although essential, is not the only requirement for a realistic numerical framework. Accurate implementation of the geometry and the dielectric properties of the simulated model is highly important, especially when it comes to high-frequency near-field scenarios such as GPR for landmine detection. In the suggested numerical scheme, both the soil’s properties as well as the rough surface are simulated using fractal correlated noise. It is shown, that fractals can sufficiently represent Earth’s topography and give rise to semi-variograms often encountered in real soils. Regarding the dielectric properties of the soils, a semi-analytic function is employed which relates soil’s dielectric properties to its sand fraction, clay fraction, sand density, bulk density and water volumetric fraction. Subsequently, the semi-analytic function is approximated using a Debye function that can be easily implemented to FDTD. Vegetation is also implemented to the model using a novel method which simulates the geometry of vegetation through a stochastic process. The experimentally-derived dielectric properties of vegetation are approximated –similarly to soil’s dielectric properties– with a Debye expansion. The antenna units tested in the numerical scheme are two bow-tie antennas based on commercially available transducers. Regarding the targets, three landmines are chosen, namely, PMN, PMA-1 and TS-50. Dummy landmines are used in order to obtain their geometrical characteristics and comparison between measured and numerically evaluated traces are used to tune the dielectric properties of the modelled landmines. Lastly, water puddles are realistically implemented in the model in an effort to realistically simulate high-saturated scenarios. The proposed numerical scheme has been employed in order to test and evaluate widely used post-processing methods. The results clearly illustrate that post-processing methods are sensitive to the antenna unit as well as the medium. This highlights the importance of an accurate numerical scheme as a testbed for evaluating different GPR systems and post-processing approaches in wide range of scenarios. Using an equivalent 2D numerical scheme –restricted to 2D due to computational constrains– preliminary results are given regarding the effectiveness of Artificial Neural Network (ANN) subject to an adequate and equally distributed database. The results are promising, showing that ANN can be successfully employed for detection as well as classification using only a single trace as input. A basic requirement to do so is a representative training set. This can be synthetically generated using a realistic numerical framework. The above, provide solid arguments for further expanding the proposed machine learning scheme to the more computationally demanding 3D case.

At least 110 million landmines have been planted since the 1970s in about 70 nations, many of which remain in place today. Some risk of detection may be mitigated using currently available remote sensing techniques when vegetation is present. My study focused on using plants as phytosensors to detect buried explosives. I exposed three species representing different functional types (Cyperus esculentus (sedge), Ulmus alata (tree), Vitis labrusca (vine)) to 500 mg kg-1 of Composition B (Comp B; 60/40 mixture of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and 2,4,6-trinitrotoluene (TNT)), a commonly used explosive mixture, and measured functional traits and reflectance over a nine-week period. Cyperus esculentus was not a good indicator for the presence of explosive compounds. Comp B treatment woody species, U. alata and V. labrusca, exhibited changes in pigment content, leaf area, specific leaf area, dry leaf biomass, and canopy reflectance. The efficacy of plants as landmine detectors is species and/or functional group dependent.